Research map / Probabilistic and scientific modeling / Treatment effects
Orthogonal Random Forest for Causal Inference
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use local orthogonal nuisance estimation with ORF rather than global residualization followed by GRF | Synthetic piecewise-linear treatment effects · Synthetic data, varying support size | RMSE | Helped |
| Select nuisance Lasso regularization by cross-validation (ablation) | ORF · Synthetic treatment effects across support sizes and treatment functions | RMSE | Helped |
| Remove sample splitting and use one forest for both estimation stages (ablation) | ORF-CV · Synthetic data, varying subsample ratio | RMSE | Hurt |
| Use Random Forests rather than Lasso for residualization | GRF-Res · Synthetic high-dimensional controls | bias | Hurt |
| Use polynomial heterogeneity in heterogeneous double machine learning | HeteroDML-Lasso and HeteroDML-RF · Synthetic piecewise-linear treatment effects | fit to treatment-effect function | Hurt |
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